Imagine asking an AI to not just write a story, but to actually perform tasks in a virtual world. Sounds simple enough, right? It turns out that getting AI to plan and execute actions, even in a simulated environment, is surprisingly difficult. While Large Language Models (LLMs) excel at understanding and generating text, they struggle with the structured reasoning required for planning. This is where the Planning Domain Definition Language (PDDL) comes in. PDDL provides a formal way to describe planning problems, allowing AI systems to use classical planning algorithms to find solutions. Think of it as a precise set of instructions that a computer can follow to achieve a goal. The challenge, however, lies in translating natural language instructions into this formal PDDL format. Traditionally, this requires human expertise, which is both time-consuming and expensive. New research proposes a solution: letting the AI learn PDDL directly by interacting with its environment. This innovative approach involves an iterative process where the LLM proposes PDDL descriptions, tests them in the environment, and refines them based on the feedback received. It's like a child learning by trial and error, but with the precision of a computer program. A key innovation is the introduction of the "Exploration Walk" (EW) metric. EW measures how well the LLM's PDDL description aligns with the actual environment. This feedback helps the LLM refine its PDDL, improving its planning accuracy over time. Experiments on 10 different planning domains show that this method significantly outperforms LLMs that attempt to plan without interacting with the environment. The results demonstrate the potential of this approach to create more reliable and autonomous AI agents. While promising, the research also highlights the challenges of predicate design in PDDL. Getting the precise definitions of objects and their relationships is crucial for accurate planning. Future research could explore more sophisticated exploration strategies and apply this framework to more complex digital and even physical environments. This work opens up exciting possibilities for the future of AI, paving the way for agents that can not only understand our commands but also effectively plan and execute actions in the real world.